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@LuisJG8 LuisJG8 commented Jan 27, 2026

…te one big task into 10 subtasks

@LuisJG8 LuisJG8 merged commit 00a0358 into MyProjects Jan 27, 2026
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Summary of Changes

Hello @LuisJG8, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request significantly enhances the system's ability to process large datasets by implementing a robust parallel task execution strategy using Celery chords. It refactors the task distribution mechanism to break down a single large data retrieval operation into smaller, concurrently executable subtasks, followed by an automated aggregation of their results. This change aims to improve scalability and efficiency when fetching data, particularly from external APIs like GitHub.

Highlights

  • Task Orchestration: Switched from using Celery's group primitive to chord for managing parallel tasks, allowing for a final aggregation step.
  • Parallel Task Creation: Introduced a new build_repo_chord function to programmatically construct a Celery chord, enabling the distribution of a large task into multiple subtasks.
  • Task Parameterization: Modified the get_github_data task to accept a batch_size parameter, providing more granular control over the amount of work each subtask processes.
  • Client Integration: Updated client.py to utilize the new build_repo_chord function for initiating the distributed data retrieval process.

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Code Review

This pull request refactors the task distribution logic to use Celery's chord primitive for parallel execution of subtasks and result aggregation. This is a good approach for distributing a large workload. My review includes a critical fix for a bug in the batching logic where a hardcoded value was used instead of the batch_size parameter. I've also pointed out some minor code cleanup opportunities, such as removing temporary test comments and suggesting the use of constants for hardcoded values to improve maintainability.


if int(counter) == 10:
print(f"Reached the rate limit of {rate_limit[1]} API calls")
if int(counter) >= int(2):
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critical

This condition appears to contain a hardcoded value 2 from testing or debugging, which will cause the task to process at most 2 repositories regardless of the batch_size. It should use the batch_size parameter to correctly limit the number of repositories processed per task. Also, casting counter to int is redundant as it's already an integer.

Suggested change
if int(counter) >= int(2):
if counter >= batch_size:


print("Waiting for Celery task to complete")

# test
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medium

This # test comment appears to be temporary and was likely added for debugging. It should be removed before merging to keep the code clean.

try:
print("Getting the result")
response = distribute_tasks.apply_async()
response = build_repo_chord(total=5000, batch_size=500)
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medium

The values for total (5000) and batch_size (500) are hardcoded here. Consider defining these as constants at the top of the module (e.g., TOTAL_REPOS_TO_FETCH = 5000, REPO_BATCH_SIZE = 500) to improve readability and make them easier to change in the future.

# the way groups can be linked is by usign the chord construct in Celery,
# this is important when the groups have an order
# groups, chords
# test
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medium

This # test comment seems to be temporary and should be removed to maintain code cleanliness.

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